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docs: Add Databricks integration (#25929)

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Updating the gateway pages in the documentation to name the
`langchain-databricks` integration.

---------

Signed-off-by: B-Step62 <yuki.watanabe@databricks.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
This commit is contained in:
Yuki Watanabe
2024-09-02 15:05:40 -07:00
committed by GitHub
parent 933bc0d6ff
commit 64dfdaa924
3 changed files with 52 additions and 4 deletions
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25
docs/docs/integrations/providers/databricks.md
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@@ -11,13 +11,22 @@ Databricks embraces the LangChain ecosystem in various ways:
4. 🌐 **SQL Database** - [Databricks SQL](https://www.databricks.com/product/databricks-sql) is integrated with `SQLDatabase` in LangChain, allowing you to access the auto-optimizing, exceptionally performant data warehouse.
5. 💡 **Open Models** - Databricks open sources models, such as [DBRX](https://www.databricks.com/blog/introducing-dbrx-new-state-art-open-llm), which are available through the [Hugging Face Hub](https://huggingface.co/databricks/dbrx-instruct). These models can be directly utilized with LangChain, leveraging its integration with the `transformers` library.
Installation
------------
First-party Databricks integrations are available in the langchain-databricks partner package.
```
pip install langchain-databricks
```
Chat Model
----------
`ChatDatabricks` is a Chat Model class to access chat endpoints hosted on Databricks, including state-of-the-art models such as Llama3, Mixtral, and DBRX, as well as your own fine-tuned models.
```
from langchain_community.chat_models.databricks import ChatDatabricks
from langchain_databricks import ChatDatabricks
chat_model = ChatDatabricks(endpoint="databricks-meta-llama-3-70b-instruct")
```
@@ -29,6 +38,9 @@ LLM
`Databricks` is an LLM class to access completion endpoints hosted on Databricks.
:::caution
Text completion models have been deprecated and the latest and most popular models are [chat completion models](/docs/concepts/#chat-models). Use `ChatDatabricks` chat model instead to use those models and advanced features such as tool calling.
```
from langchain_community.llm.databricks import Databricks
@@ -44,7 +56,7 @@ Embeddings
`DatabricksEmbeddings` is an Embeddings class to access text-embedding endpoints hosted on Databricks, including state-of-the-art models such as BGE, as well as your own fine-tuned models.
```
from langchain_community.embeddings import DatabricksEmbeddings
from langchain_databricks import DatabricksEmbeddings
embeddings = DatabricksEmbeddings(endpoint="databricks-bge-large-en")
```
@@ -58,10 +70,15 @@ Vector Search
Databricks Vector Search is a serverless similarity search engine that allows you to store a vector representation of your data, including metadata, in a vector database. With Vector Search, you can create auto-updating vector search indexes from [Delta](https://docs.databricks.com/en/introduction/delta-comparison.html) tables managed by [Unity Catalog](https://www.databricks.com/product/unity-catalog) and query them with a simple API to return the most similar vectors.
```
from langchain_community.vectorstores import DatabricksVectorSearch
from langchain_databricks.vectorstores import DatabricksVectorSearch
dvs = DatabricksVectorSearch(
index, text_column="text", embedding=embeddings, columns=["source"]
endpoint="<YOUT_ENDPOINT_NAME>",
index_name="<YOUR_INDEX_NAME>",
index,
text_column="text",
embedding=embeddings,
columns=["source"]
)
docs = dvs.similarity_search("What is vector search?)
```